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Issue title: Complex evolutionary artificial intelligence in cognitive digital twinning
Guest editors: Neal Wagner, Sundhararajan, Le Hoang Son and Meng Joo
Article type: Research Article
Affiliations: The School of Physical Education, Shandong Normal University, Jinan Shandong, China
Correspondence: [*] Corresponding author. Yanan Yu, The School of Physical Education, Shandong Normal University, Jinan Shandong, China. E-mail: [email protected].
Abstract: EMG signal acquisition is mostly used in medical research. However, it has not been applied in athletes’ sports state recognition and body state detection, and there are few related studies at present. In order to promote the application of EMG signal acquisition in sports, this study combined with the actual needs of athletes to construct an EMG signal acquisition system that can collect athletes’ motion status. At the same time, in order to improve the effect of EMG signal acquisition, a wavelet packet principal component analysis model is proposed. In addition, in order to ensure the recognition efficiency of athletes’ motion state, this paper uses linear discriminant analysis method as the motion recognition assistant algorithm. Finally, this paper judges the performance of this research model by setting up comparative experiments. The research shows that the wavelet packet principal component analysis model performance is significantly better than the traditional algorithm, and the recognition rate for some subtle motions is also high. In addition, this study provides a theoretical reference for the application of EMG signals in the sports industry.
Keywords: EMG signal acquisition, athlete, wavelet packet master analysis, motion recognition
DOI: 10.3233/JIFS-189220
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 2217-2227, 2021
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